Overview

Dataset statistics

Number of variables17
Number of observations2524
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory419.5 KiB
Average record size in memory170.2 B

Variable types

Numeric8
Categorical6
DateTime1
Boolean2

Alerts

country has constant value "Australia"Constant
job_title has a high cardinality: 195 distinct valuesHigh cardinality
postcode is highly overall correlated with property_valuation and 1 other fieldsHigh correlation
property_valuation is highly overall correlated with postcodeHigh correlation
list_price is highly overall correlated with standard_costHigh correlation
standard_cost is highly overall correlated with list_priceHigh correlation
state is highly overall correlated with postcodeHigh correlation
deceased_indicator is highly imbalanced (99.5%)Imbalance
customer_id has unique valuesUnique
past_3_years_bike_related_purchases has 27 (1.1%) zerosZeros

Reproduction

Analysis started2023-06-12 14:02:38.900613
Analysis finished2023-06-12 14:03:02.710659
Duration23.81 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2524
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731.2254
Minimum1
Maximum3497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:02.891645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile173.15
Q1848.75
median1726.5
Q32600.25
95-th percentile3314.4
Maximum3497
Range3496
Interquartile range (IQR)1751.5

Descriptive statistics

Standard deviation1009.0326
Coefficient of variation (CV)0.58284296
Kurtosis-1.1957022
Mean1731.2254
Median Absolute Deviation (MAD)876
Skewness0.013907781
Sum4369613
Variance1018146.7
MonotonicityStrictly increasing
2023-06-12T14:03:03.193892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2315 1
 
< 0.1%
2307 1
 
< 0.1%
2308 1
 
< 0.1%
2309 1
 
< 0.1%
2311 1
 
< 0.1%
2312 1
 
< 0.1%
2313 1
 
< 0.1%
2314 1
 
< 0.1%
2316 1
 
< 0.1%
Other values (2514) 2514
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
3497 1
< 0.1%
3496 1
< 0.1%
3495 1
< 0.1%
3494 1
< 0.1%
3493 1
< 0.1%
3492 1
< 0.1%
3491 1
< 0.1%
3490 1
< 0.1%
3489 1
< 0.1%
3488 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Female
1286 
Male
1238 

Length

Max length6
Median length6
Mean length5.0190174
Min length4

Characters and Unicode

Total characters12668
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 1286
51.0%
Male 1238
49.0%

Length

2023-06-12T14:03:03.503845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T14:03:03.778492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 1286
51.0%
male 1238
49.0%

Most occurring characters

ValueCountFrequency (%)
e 3810
30.1%
a 2524
19.9%
l 2524
19.9%
F 1286
 
10.2%
m 1286
 
10.2%
M 1238
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10144
80.1%
Uppercase Letter 2524
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3810
37.6%
a 2524
24.9%
l 2524
24.9%
m 1286
 
12.7%
Uppercase Letter
ValueCountFrequency (%)
F 1286
51.0%
M 1238
49.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12668
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3810
30.1%
a 2524
19.9%
l 2524
19.9%
F 1286
 
10.2%
m 1286
 
10.2%
M 1238
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3810
30.1%
a 2524
19.9%
l 2524
19.9%
F 1286
 
10.2%
m 1286
 
10.2%
M 1238
 
9.8%
Distinct100
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.40729
Minimum0
Maximum99
Zeros27
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:04.017394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median48
Q373
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.607678
Coefficient of variation (CV)0.57901735
Kurtosis-1.1593779
Mean49.40729
Median Absolute Deviation (MAD)24
Skewness0.032630199
Sum124704
Variance818.39925
MonotonicityNot monotonic
2023-06-12T14:03:04.814132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37
 
1.5%
67 37
 
1.5%
48 35
 
1.4%
98 33
 
1.3%
35 33
 
1.3%
97 33
 
1.3%
80 33
 
1.3%
33 33
 
1.3%
53 33
 
1.3%
30 32
 
1.3%
Other values (90) 2185
86.6%
ValueCountFrequency (%)
0 27
1.1%
1 16
0.6%
2 37
1.5%
3 17
0.7%
4 24
1.0%
5 20
0.8%
6 29
1.1%
7 26
1.0%
8 15
0.6%
9 23
0.9%
ValueCountFrequency (%)
99 26
1.0%
98 33
1.3%
97 33
1.3%
96 31
1.2%
95 19
0.8%
94 29
1.1%
93 29
1.1%
92 14
0.6%
91 21
0.8%
90 21
0.8%

DOB
Date

Distinct2323
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Minimum1931-10-23 00:00:00
Maximum2002-03-11 00:00:00
2023-06-12T14:03:05.133009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:03:05.421317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ)

Distinct52
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.078447
Minimum21
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:05.700393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile24
Q136
median45
Q354
95-th percentile66
Maximum91
Range70
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.498251
Coefficient of variation (CV)0.27725559
Kurtosis-0.72416444
Mean45.078447
Median Absolute Deviation (MAD)9
Skewness0.032449955
Sum113778
Variance156.20629
MonotonicityNot monotonic
2023-06-12T14:03:06.022832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 157
 
6.2%
46 117
 
4.6%
44 109
 
4.3%
49 98
 
3.9%
48 96
 
3.8%
42 76
 
3.0%
47 74
 
2.9%
37 73
 
2.9%
43 73
 
2.9%
35 56
 
2.2%
Other values (42) 1595
63.2%
ValueCountFrequency (%)
21 16
 
0.6%
22 24
1.0%
23 36
1.4%
24 54
2.1%
25 51
2.0%
26 40
1.6%
27 47
1.9%
28 52
2.1%
29 43
1.7%
30 50
2.0%
ValueCountFrequency (%)
91 1
 
< 0.1%
87 1
 
< 0.1%
79 2
 
0.1%
69 32
1.3%
68 33
1.3%
67 38
1.5%
66 36
1.4%
65 35
1.4%
64 48
1.9%
63 46
1.8%

job_title
Categorical

Distinct195
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Social Worker
 
36
Chemical Engineer
 
34
Registered Nurse
 
32
Nuclear Power Engineer
 
32
Assistant Media Planner
 
32
Other values (190)
2358 

Length

Max length36
Median length25
Mean length18.076862
Min length5

Characters and Unicode

Total characters45626
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowExecutive Secretary
2nd rowAdministrative Officer
3rd rowBusiness Systems Development Analyst
4th rowNuclear Power Engineer
5th rowDeveloper I

Common Values

ValueCountFrequency (%)
Social Worker 36
 
1.4%
Chemical Engineer 34
 
1.3%
Registered Nurse 32
 
1.3%
Nuclear Power Engineer 32
 
1.3%
Assistant Media Planner 32
 
1.3%
Internal Auditor 31
 
1.2%
Dental Hygienist 31
 
1.2%
Legal Assistant 30
 
1.2%
Research Nurse 30
 
1.2%
Desktop Support Technician 29
 
1.1%
Other values (185) 2207
87.4%

Length

2023-06-12T14:03:06.337992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
engineer 364
 
6.2%
assistant 238
 
4.1%
analyst 190
 
3.2%
manager 189
 
3.2%
iv 150
 
2.6%
i 148
 
2.5%
iii 142
 
2.4%
ii 133
 
2.3%
senior 126
 
2.1%
systems 121
 
2.1%
Other values (117) 4073
69.3%

Most occurring characters

ValueCountFrequency (%)
e 4469
 
9.8%
n 3500
 
7.7%
t 3481
 
7.6%
a 3425
 
7.5%
i 3358
 
7.4%
3350
 
7.3%
r 2963
 
6.5%
s 2769
 
6.1%
o 2098
 
4.6%
c 2018
 
4.4%
Other values (37) 14195
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35692
78.2%
Uppercase Letter 6562
 
14.4%
Space Separator 3350
 
7.3%
Other Punctuation 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4469
12.5%
n 3500
9.8%
t 3481
9.8%
a 3425
9.6%
i 3358
9.4%
r 2963
8.3%
s 2769
7.8%
o 2098
 
5.9%
c 2018
 
5.7%
l 1467
 
4.1%
Other values (14) 6144
17.2%
Uppercase Letter
ValueCountFrequency (%)
I 1060
16.2%
A 1017
15.5%
S 789
12.0%
E 528
8.0%
P 492
7.5%
C 393
 
6.0%
D 336
 
5.1%
M 310
 
4.7%
V 263
 
4.0%
T 238
 
3.6%
Other values (11) 1136
17.3%
Space Separator
ValueCountFrequency (%)
3350
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42254
92.6%
Common 3372
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4469
 
10.6%
n 3500
 
8.3%
t 3481
 
8.2%
a 3425
 
8.1%
i 3358
 
7.9%
r 2963
 
7.0%
s 2769
 
6.6%
o 2098
 
5.0%
c 2018
 
4.8%
l 1467
 
3.5%
Other values (35) 12706
30.1%
Common
ValueCountFrequency (%)
3350
99.3%
/ 22
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4469
 
9.8%
n 3500
 
7.7%
t 3481
 
7.6%
a 3425
 
7.5%
i 3358
 
7.4%
3350
 
7.3%
r 2963
 
6.5%
s 2769
 
6.1%
o 2098
 
4.6%
c 2018
 
4.4%
Other values (37) 14195
31.1%
Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Manufacturing
615 
Financial Services
607 
Health
480 
Retail
260 
Property
202 
Other values (4)
360 

Length

Max length18
Median length13
Mean length11.317353
Min length2

Characters and Unicode

Total characters28565
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealth
2nd rowFinancial Services
3rd rowArgiculture
4th rowManufacturing
5th rowFinancial Services

Common Values

ValueCountFrequency (%)
Manufacturing 615
24.4%
Financial Services 607
24.0%
Health 480
19.0%
Retail 260
10.3%
Property 202
 
8.0%
Entertainment 111
 
4.4%
IT 108
 
4.3%
Argiculture 87
 
3.4%
Telecommunications 54
 
2.1%

Length

2023-06-12T14:03:06.639655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T14:03:06.956620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manufacturing 615
19.6%
financial 607
19.4%
services 607
19.4%
health 480
15.3%
retail 260
8.3%
property 202
 
6.5%
entertainment 111
 
3.5%
it 108
 
3.4%
argiculture 87
 
2.8%
telecommunications 54
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 3349
11.7%
i 3002
10.5%
n 2885
10.1%
e 2573
 
9.0%
t 2031
 
7.1%
c 2024
 
7.1%
r 1911
 
6.7%
l 1488
 
5.2%
u 1458
 
5.1%
g 702
 
2.5%
Other values (19) 7142
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24719
86.5%
Uppercase Letter 3239
 
11.3%
Space Separator 607
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3349
13.5%
i 3002
12.1%
n 2885
11.7%
e 2573
10.4%
t 2031
8.2%
c 2024
8.2%
r 1911
7.7%
l 1488
6.0%
u 1458
5.9%
g 702
 
2.8%
Other values (8) 3296
13.3%
Uppercase Letter
ValueCountFrequency (%)
M 615
19.0%
F 607
18.7%
S 607
18.7%
H 480
14.8%
R 260
8.0%
P 202
 
6.2%
T 162
 
5.0%
E 111
 
3.4%
I 108
 
3.3%
A 87
 
2.7%
Space Separator
ValueCountFrequency (%)
607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27958
97.9%
Common 607
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3349
12.0%
i 3002
10.7%
n 2885
10.3%
e 2573
 
9.2%
t 2031
 
7.3%
c 2024
 
7.2%
r 1911
 
6.8%
l 1488
 
5.3%
u 1458
 
5.2%
g 702
 
2.5%
Other values (18) 6535
23.4%
Common
ValueCountFrequency (%)
607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3349
11.7%
i 3002
10.5%
n 2885
10.1%
e 2573
 
9.0%
t 2031
 
7.1%
c 2024
 
7.1%
r 1911
 
6.7%
l 1488
 
5.2%
u 1458
 
5.1%
g 702
 
2.5%
Other values (19) 7142
25.0%

wealth_segment
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Mass Customer
1249 
High Net Worth
650 
Affluent Customer
625 

Length

Max length17
Median length14
Mean length14.248019
Min length13

Characters and Unicode

Total characters35962
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMass Customer
2nd rowMass Customer
3rd rowAffluent Customer
4th rowMass Customer
5th rowHigh Net Worth

Common Values

ValueCountFrequency (%)
Mass Customer 1249
49.5%
High Net Worth 650
25.8%
Affluent Customer 625
24.8%

Length

2023-06-12T14:03:07.291263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T14:03:07.573746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 1874
32.9%
mass 1249
21.9%
high 650
 
11.4%
net 650
 
11.4%
worth 650
 
11.4%
affluent 625
 
11.0%

Most occurring characters

ValueCountFrequency (%)
s 4372
12.2%
t 3799
10.6%
3174
 
8.8%
e 3149
 
8.8%
r 2524
 
7.0%
o 2524
 
7.0%
u 2499
 
6.9%
C 1874
 
5.2%
m 1874
 
5.2%
h 1300
 
3.6%
Other values (11) 8873
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27090
75.3%
Uppercase Letter 5698
 
15.8%
Space Separator 3174
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4372
16.1%
t 3799
14.0%
e 3149
11.6%
r 2524
9.3%
o 2524
9.3%
u 2499
9.2%
m 1874
6.9%
h 1300
 
4.8%
f 1250
 
4.6%
a 1249
 
4.6%
Other values (4) 2550
9.4%
Uppercase Letter
ValueCountFrequency (%)
C 1874
32.9%
M 1249
21.9%
H 650
 
11.4%
N 650
 
11.4%
W 650
 
11.4%
A 625
 
11.0%
Space Separator
ValueCountFrequency (%)
3174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32788
91.2%
Common 3174
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4372
13.3%
t 3799
11.6%
e 3149
9.6%
r 2524
 
7.7%
o 2524
 
7.7%
u 2499
 
7.6%
C 1874
 
5.7%
m 1874
 
5.7%
h 1300
 
4.0%
f 1250
 
3.8%
Other values (10) 7623
23.2%
Common
ValueCountFrequency (%)
3174
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4372
12.2%
t 3799
10.6%
3174
 
8.8%
e 3149
 
8.8%
r 2524
 
7.0%
o 2524
 
7.0%
u 2499
 
6.9%
C 1874
 
5.2%
m 1874
 
5.2%
h 1300
 
3.6%
Other values (11) 8873
24.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
False
2523 
True
 
1
ValueCountFrequency (%)
False 2523
> 99.9%
True 1
 
< 0.1%
2023-06-12T14:03:07.905590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

owns_car
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
True
1288 
False
1236 
ValueCountFrequency (%)
True 1288
51.0%
False 1236
49.0%
2023-06-12T14:03:08.310958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

tenure
Real number (ℝ)

Distinct22
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.719097
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:08.728851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6811096
Coefficient of variation (CV)0.52999891
Kurtosis-1.04804
Mean10.719097
Median Absolute Deviation (MAD)5
Skewness0.049897068
Sum27055
Variance32.275006
MonotonicityNot monotonic
2023-06-12T14:03:09.209987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7 146
 
5.8%
16 145
 
5.7%
11 142
 
5.6%
12 142
 
5.6%
8 141
 
5.6%
5 139
 
5.5%
14 136
 
5.4%
18 132
 
5.2%
9 132
 
5.2%
10 131
 
5.2%
Other values (12) 1138
45.1%
ValueCountFrequency (%)
1 111
4.4%
2 95
3.8%
3 106
4.2%
4 118
4.7%
5 139
5.5%
6 119
4.7%
7 146
5.8%
8 141
5.6%
9 132
5.2%
10 131
5.2%
ValueCountFrequency (%)
22 38
 
1.5%
21 38
 
1.5%
20 64
2.5%
19 106
4.2%
18 132
5.2%
17 113
4.5%
16 145
5.7%
15 101
4.0%
14 136
5.4%
13 129
5.1%

postcode
Real number (ℝ)

Distinct762
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2993.8879
Minimum2000
Maximum4883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:09.710960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2049
Q12196.75
median2767
Q33787.25
95-th percentile4551
Maximum4883
Range2883
Interquartile range (IQR)1590.5

Descriptive statistics

Standard deviation857.19613
Coefficient of variation (CV)0.28631538
Kurtosis-0.95621377
Mean2993.8879
Median Absolute Deviation (MAD)601
Skewness0.61140107
Sum7556573
Variance734785.21
MonotonicityNot monotonic
2023-06-12T14:03:10.227502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2155 20
 
0.8%
2170 20
 
0.8%
2153 19
 
0.8%
2145 19
 
0.8%
2166 16
 
0.6%
3977 16
 
0.6%
2567 16
 
0.6%
2148 15
 
0.6%
2759 14
 
0.6%
2261 14
 
0.6%
Other values (752) 2355
93.3%
ValueCountFrequency (%)
2000 6
0.2%
2007 2
 
0.1%
2008 1
 
< 0.1%
2009 3
 
0.1%
2010 8
0.3%
2011 1
 
< 0.1%
2015 5
0.2%
2016 5
0.2%
2017 5
0.2%
2018 3
 
0.1%
ValueCountFrequency (%)
4883 1
 
< 0.1%
4878 3
0.1%
4877 1
 
< 0.1%
4873 1
 
< 0.1%
4870 7
0.3%
4869 5
0.2%
4868 3
0.1%
4860 1
 
< 0.1%
4825 5
0.2%
4820 4
0.2%

state
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
NSW
1346 
VIC
575 
QLD
546 
Victoria
 
57

Length

Max length8
Median length3
Mean length3.112916
Min length3

Characters and Unicode

Total characters7857
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNSW
2nd rowNSW
3rd rowNSW
4th rowQLD
5th rowVIC

Common Values

ValueCountFrequency (%)
NSW 1346
53.3%
VIC 575
22.8%
QLD 546
21.6%
Victoria 57
 
2.3%

Length

2023-06-12T14:03:10.760330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T14:03:11.283454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nsw 1346
53.3%
vic 575
22.8%
qld 546
21.6%
victoria 57
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N 1346
17.1%
S 1346
17.1%
W 1346
17.1%
V 632
8.0%
I 575
7.3%
C 575
7.3%
Q 546
6.9%
L 546
6.9%
D 546
6.9%
i 114
 
1.5%
Other values (5) 285
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7458
94.9%
Lowercase Letter 399
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1346
18.0%
S 1346
18.0%
W 1346
18.0%
V 632
8.5%
I 575
7.7%
C 575
7.7%
Q 546
7.3%
L 546
7.3%
D 546
7.3%
Lowercase Letter
ValueCountFrequency (%)
i 114
28.6%
c 57
14.3%
t 57
14.3%
o 57
14.3%
r 57
14.3%
a 57
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7857
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1346
17.1%
S 1346
17.1%
W 1346
17.1%
V 632
8.0%
I 575
7.3%
C 575
7.3%
Q 546
6.9%
L 546
6.9%
D 546
6.9%
i 114
 
1.5%
Other values (5) 285
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1346
17.1%
S 1346
17.1%
W 1346
17.1%
V 632
8.0%
I 575
7.3%
C 575
7.3%
Q 546
6.9%
L 546
6.9%
D 546
6.9%
i 114
 
1.5%
Other values (5) 285
 
3.6%

country
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
Australia
2524 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters22716
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Australia 2524
100.0%

Length

2023-06-12T14:03:11.506630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T14:03:11.738378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
australia 2524
100.0%

Most occurring characters

ValueCountFrequency (%)
a 5048
22.2%
A 2524
11.1%
u 2524
11.1%
s 2524
11.1%
t 2524
11.1%
r 2524
11.1%
l 2524
11.1%
i 2524
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20192
88.9%
Uppercase Letter 2524
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5048
25.0%
u 2524
12.5%
s 2524
12.5%
t 2524
12.5%
r 2524
12.5%
l 2524
12.5%
i 2524
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 2524
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22716
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5048
22.2%
A 2524
11.1%
u 2524
11.1%
s 2524
11.1%
t 2524
11.1%
r 2524
11.1%
l 2524
11.1%
i 2524
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5048
22.2%
A 2524
11.1%
u 2524
11.1%
s 2524
11.1%
t 2524
11.1%
r 2524
11.1%
l 2524
11.1%
i 2524
11.1%

property_valuation
Real number (ℝ)

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.479794
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:11.925286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median8
Q310
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8329057
Coefficient of variation (CV)0.37874114
Kurtosis-0.36388935
Mean7.479794
Median Absolute Deviation (MAD)2
Skewness-0.62675131
Sum18879
Variance8.0253545
MonotonicityNot monotonic
2023-06-12T14:03:12.153199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 422
16.7%
8 410
16.2%
10 354
14.0%
7 296
11.7%
11 174
6.9%
6 151
 
6.0%
5 149
 
5.9%
4 146
 
5.8%
12 119
 
4.7%
3 108
 
4.3%
Other values (2) 195
7.7%
ValueCountFrequency (%)
1 102
 
4.0%
2 93
 
3.7%
3 108
 
4.3%
4 146
 
5.8%
5 149
 
5.9%
6 151
 
6.0%
7 296
11.7%
8 410
16.2%
9 422
16.7%
10 354
14.0%
ValueCountFrequency (%)
12 119
 
4.7%
11 174
6.9%
10 354
14.0%
9 422
16.7%
8 410
16.2%
7 296
11.7%
6 151
 
6.0%
5 149
 
5.9%
4 146
 
5.8%
3 108
 
4.3%

list_price
Real number (ℝ)

Distinct2518
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1108.6353
Minimum60.34
Maximum2091.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:12.425017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60.34
5-th percentile659.79133
Q1930.76075
median1112.735
Q31285.3882
95-th percentile1564.9388
Maximum2091.47
Range2031.13
Interquartile range (IQR)354.6275

Descriptive statistics

Standard deviation282.60337
Coefficient of variation (CV)0.25491103
Kurtosis0.54084619
Mean1108.6353
Median Absolute Deviation (MAD)177.451
Skewness-0.085698705
Sum2798195.6
Variance79864.667
MonotonicityNot monotonic
2023-06-12T14:03:12.731329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005.66 2
 
0.1%
774.53 2
 
0.1%
441.49 2
 
0.1%
681.23 2
 
0.1%
1018.74 2
 
0.1%
937.575 2
 
0.1%
1275.174 1
 
< 0.1%
1348.43 1
 
< 0.1%
1202.338 1
 
< 0.1%
1234.66 1
 
< 0.1%
Other values (2508) 2508
99.4%
ValueCountFrequency (%)
60.34 1
< 0.1%
71.49 1
< 0.1%
100.35 1
< 0.1%
127.675 1
< 0.1%
139.6033333 1
< 0.1%
153.395 1
< 0.1%
180.89 1
< 0.1%
193.24 1
< 0.1%
202.5933333 1
< 0.1%
202.62 1
< 0.1%
ValueCountFrequency (%)
2091.47 1
< 0.1%
2083.94 1
< 0.1%
2023.004 1
< 0.1%
2005.66 2
0.1%
1977.36 1
< 0.1%
1952.11 1
< 0.1%
1936.856667 1
< 0.1%
1934.635 1
< 0.1%
1930.87 1
< 0.1%
1893.73 1
< 0.1%

standard_cost
Real number (ℝ)

Distinct2513
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean559.29735
Minimum44.985
Maximum1759.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2023-06-12T14:03:13.058371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum44.985
5-th percentile272.53917
Q1426.42325
median550.25746
Q3675.42994
95-th percentile878.60517
Maximum1759.85
Range1714.865
Interquartile range (IQR)249.00669

Descriptive statistics

Standard deviation192.94416
Coefficient of variation (CV)0.34497599
Kurtosis1.5680607
Mean559.29735
Median Absolute Deviation (MAD)124.47078
Skewness0.61090109
Sum1411666.5
Variance37227.448
MonotonicityNot monotonic
2023-06-12T14:03:13.362724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
827.8933333 2
 
0.1%
561.555 2
 
0.1%
528.43 2
 
0.1%
602.4766667 2
 
0.1%
593.82 2
 
0.1%
246.6266667 2
 
0.1%
1203.4 2
 
0.1%
448.9 2
 
0.1%
792.64625 2
 
0.1%
464.72 2
 
0.1%
Other values (2503) 2504
99.2%
ValueCountFrequency (%)
44.985 1
< 0.1%
45.26 1
< 0.1%
53.62 1
< 0.1%
55.68 1
< 0.1%
74.51 1
< 0.1%
75.26 1
< 0.1%
84.99 2
0.1%
91 1
< 0.1%
95.76 1
< 0.1%
101.1325 1
< 0.1%
ValueCountFrequency (%)
1759.85 1
< 0.1%
1610.9 1
< 0.1%
1459.415 1
< 0.1%
1356.5475 1
< 0.1%
1335.423333 1
< 0.1%
1323.145 1
< 0.1%
1288.93 1
< 0.1%
1287.978 1
< 0.1%
1259.36 1
< 0.1%
1234.156667 1
< 0.1%

Interactions

2023-06-12T14:02:58.185133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:40.174793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:45.938365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:48.082091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:50.073811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:52.576541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:55.040918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:58.733231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:41.425584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:46.487556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:48.596250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:51.020106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:53.084896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:55.892240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:59.119651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:41.809502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:46.749310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:48.851794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:51.290556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:53.341076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:56.329038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:59.530080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:42.188183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:46.990196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:49.084706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:51.547697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:53.594559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:56.700757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:59.967864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:42.589167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:47.263036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:49.339019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:51.796563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:53.847667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:57.158053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:03:00.415084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:42.972738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:47.522537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:49.575379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:52.050140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:54.205834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:57.607694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:03:00.837615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:43.381207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:47.814877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:49.828866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:52.312896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:54.609427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T14:02:57.900246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T14:03:13.625261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idpast_3_years_bike_related_purchasesagetenurepostcodeproperty_valuationlist_pricestandard_costgenderjob_industry_categorywealth_segmentdeceased_indicatorowns_carstate
customer_id1.000-0.002-0.001-0.0150.030-0.0140.0290.0240.0040.0250.0240.0160.0480.244
past_3_years_bike_related_purchases-0.0021.000-0.047-0.015-0.0100.0090.012-0.0160.0380.0000.0000.0170.0000.000
age-0.001-0.0471.0000.424-0.0230.015-0.0000.0010.0000.0000.0000.0000.0000.051
tenure-0.015-0.0150.4241.000-0.003-0.0240.001-0.0360.0350.0180.0000.0000.0140.000
postcode0.030-0.010-0.023-0.0031.000-0.5780.0260.0110.0330.0000.0000.0290.0000.793
property_valuation-0.0140.0090.015-0.024-0.5781.000-0.0130.0090.0000.0000.0140.0730.0210.254
list_price0.0290.012-0.0000.0010.026-0.0131.0000.5300.0300.0000.0000.0450.0790.000
standard_cost0.024-0.0160.001-0.0360.0110.0090.5301.0000.0000.0080.0000.0000.0120.000
gender0.0040.0380.0000.0350.0330.0000.0300.0001.0000.0000.0190.0000.0000.000
job_industry_category0.0250.0000.0000.0180.0000.0000.0000.0080.0001.0000.0210.0000.0190.021
wealth_segment0.0240.0000.0000.0000.0000.0140.0000.0000.0190.0211.0000.0200.0210.000
deceased_indicator0.0160.0170.0000.0000.0290.0730.0450.0000.0000.0000.0201.0000.0000.000
owns_car0.0480.0000.0000.0140.0000.0210.0790.0120.0000.0190.0210.0001.0000.000
state0.2440.0000.0510.0000.7930.2540.0000.0000.0000.0210.0000.0000.0001.000

Missing values

2023-06-12T14:03:01.486623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T14:03:02.474152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgenderpast_3_years_bike_related_purchasesDOBagejob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenurepostcodestatecountryproperty_valuationlist_pricestandard_cost
01Female931953-10-1269Executive SecretaryHealthMass CustomerNYes11.02016NSWAustralia10825.859091551.487273
12Male811980-12-1642Administrative OfficerFinancial ServicesMass CustomerNYes16.02153NSWAustralia101383.023333640.936667
29Female971973-03-1050Business Systems Development AnalystArgicultureAffluent CustomerNYes8.02023NSWAustralia12892.925000500.740000
312Male581994-07-2128Nuclear Power EngineerManufacturingMass CustomerNNo8.04557QLDAustralia4913.458571407.740000
413Male381955-02-1568Developer IFinancial ServicesHigh Net WorthNYes8.03799VICAustralia61104.962857485.337143
514Female851983-03-2540Account ExecutiveFinancial ServicesAffluent CustomerNNo6.02760NSWAustralia8898.392500265.163333
615Male912000-07-1322Junior ExecutiveManufacturingMass CustomerNNo1.02428NSWAustralia9820.556667532.491667
719Female762001-04-1522Geological EngineerManufacturingHigh Net WorthNNo1.02233NSWAustralia91412.730000428.800000
820Male721980-08-1342Project ManagerManufacturingMass CustomerNNo11.02444NSWAustralia81654.715000752.645000
921Male741980-09-2042Safety Technician IManufacturingAffluent CustomerNYes16.02650NSWAustralia71489.242000643.360000
customer_idgenderpast_3_years_bike_related_purchasesDOBagejob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenurepostcodestatecountryproperty_valuationlist_pricestandard_cost
25143488Male631975-08-0747Speech PathologistManufacturingMass CustomerNYes9.02136NSWAustralia101083.895000675.920000
25153489Female901969-10-3053Help Desk OperatorArgicultureMass CustomerNYes9.02099NSWAustralia111383.358333942.618333
25163490Male451980-06-2142VP Product ManagementFinancial ServicesHigh Net WorthNNo14.02126NSWAustralia10947.940000472.026000
25173491Female691976-04-0347Business Systems Development AnalystFinancial ServicesAffluent CustomerNNo10.03195VICAustralia10787.917500430.347500
25183492Male831966-01-2757Civil EngineerManufacturingMass CustomerNNo19.03021VICAustralia91380.476667649.206667
25193493Male301964-02-2959Research Assistant IHealthHigh Net WorthNNo18.02090NSWAustralia101675.6366671054.156667
25203494Male721998-12-2424Account Representative IVArgicultureHigh Net WorthNNo1.02033NSWAustralia101280.677500591.900000
25213495Female571987-07-1235Programmer IIIFinancial ServicesHigh Net WorthNNo8.02767NSWAustralia91232.378571682.714286
25223496Male991986-04-2537EditorManufacturingMass CustomerNYes19.02171NSWAustralia91181.345000669.885000
25233497Female731986-05-0337Administrative Assistant IVManufacturingAffluent CustomerNYes18.03976VICAustralia51248.023333698.583333